| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 9653585 | Neurocomputing | 2005 | 7 Pages |
Abstract
The natural gradient and fixed-point algorithm are two of the most popular algorithms in independent component analysis (ICA). However, there still remain some problems to be solved in application to the processing of the functional magnetic resonance imaging (fMRI) data. Based on the BFGS quasi-Newton algorithm, this paper presents a novel BFGS-ICA algorithm framework in performing localization of brain activities with fMRI data. The new BFGS-ICA algorithm possesses properties of good convergence and immunity of initial point sensitivity. The convincing results of its application in fMRI show the potential of BFGS-ICA in detection of the brain activities.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Huafu Chen, Dezhong Yao, Ling Zeng,